scholarly journals T-cell Receptor Diversity Estimates for Repertoires (TCRDivER) uses sequence similarity to find signatures of immune response

2021 ◽  
Author(s):  
Milena Vujović ◽  
Paolo Marcatili ◽  
Benny Chain ◽  
Joseph Kaplinsky ◽  
Thomas Lars Andresen
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Sequence Similarity ◽  
Cell Receptor ◽  
Sequence Motifs ◽  
Sequencing Data ◽  
Cell Repertoire ◽  
Specific Sequence ◽  
Clone Size ◽  
Diversity Estimates

AbstractWe propose TCRDivER, a global approach to T-cell repertoire comparison using diversity profiles sensitive to both clone size and sequence similarity. As immunotherapies improve, the long standing biological interest in connecting outcome with T cell receptor (TCR) repertoire status has become more urgent. Here we show that new insights can be extracted from high throughput repertoire sequencing data. Most current efforts focus on identification of immunisation-specific sequence motifs or on monitoring changes in frequency of individual clones. Applying TCRDivER to murine spleen samples shows it characterises an additional dimension of repertoire variation, beyond conventional diversity estimates, allowing distinction between immunised and non-immunised samples. We further apply TCRDivER to repertoires from human blood. In both cases we show characteristic relationships between repertoire features. These reveal biologically interpretable relationships between sequence similarity and clonal expansions. We thereby demonstrate a new tool for investigation in clinical and research applications.

scholarly journals T cell receptor repertoires of mice and humans are clustered in similarity networks around conserved public CDR3 sequences

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Asaf Madi ◽  
Asaf Poran ◽  
Eric Shifrut ◽  
Shlomit Reich-Zeliger ◽  
Erez Greenstein ◽  
...  
Keyword(s):  
Immune System ◽  
T Cell ◽  
T Cell Receptor ◽  
Sequence Similarity ◽  
Cell Receptor ◽  
Immune Checkpoint Blockade ◽  
Sequencing Data ◽  
Cell Repertoire ◽  
Similarity Networks ◽  
Sequence Similarity Networks

Diversity of T cell receptor (TCR) repertoires, generated by somatic DNA rearrangements, is central to immune system function. However, the level of sequence similarity of TCR repertoires within and between species has not been characterized. Using network analysis of high-throughput TCR sequencing data, we found that abundant CDR3-TCRβ sequences were clustered within networks generated by sequence similarity. We discovered a substantial number of public CDR3-TCRβ segments that were identical in mice and humans. These conserved public sequences were central within TCR sequence-similarity networks. Annotated TCR sequences, previously associated with self-specificities such as autoimmunity and cancer, were linked to network clusters. Mechanistically, CDR3 networks were promoted by MHC-mediated selection, and were reduced following immunization, immune checkpoint blockade or aging. Our findings provide a new view of T cell repertoire organization and physiology, and suggest that the immune system distributes its TCR sequences unevenly, attending to specific foci of reactivity.

scholarly journals DeepTCR: a deep learning framework for understanding T-cell receptor sequence signatures within complex T-cell repertoires

2018 ◽  
Author(s):  
John-William Sidhom ◽  
H. Benjamin Larman ◽  
Petra Ross-MacDonald ◽  
Megan Wind-Rotolo ◽  
Drew M. Pardoll ◽  
...  
Keyword(s):  
Deep Learning ◽  
T Cell ◽  
T Cell Receptor ◽  
Adaptive Immune System ◽  
Cell Receptor ◽  
Sequence Motifs ◽  
Sequencing Data ◽  
Gene Usage ◽  
Complex Structural ◽  
Pre Treatment

Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks, such as in image and vocal recognition 1,2. The ability to learn complex patterns in data has tremendous implications in the genomics and immunology worlds, where sequence motifs become learned ‘features’ that can be used to predict functionality, guiding our understanding of disease and basic biology 3–6. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system, where complex structural patterns in the TCR can be used to model its antigenic interaction. We present DeepTCR, a broad collection of unsupervised and supervised deep learning methods able to uncover structure in highly complex and large TCR sequencing data by learning a joint representation of a given TCR by its CDR3 sequences, V/D/J gene usage, and HLA background in which the T-cells reside. We demonstrate the utility of deep learning to provide an improved ‘featurization’ of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCR’s in both unsupervised and supervised learning tasks, understanding immunotherapy-related shaping of repertoire in the murine setting, and predicting response to checkpoint blockade immunotherapy from pre-treatment tumor biopsies in a clinical trial of melanoma. Our results show the flexibility and capacity for deep neural networks to handle the complexity of high-dimensional TCR genomic data for both descriptive and predictive purposes across basic science and clinical research.

scholarly journals The naive T-cell receptor repertoire has an extremely broad distribution of clone sizes

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Peter C de Greef ◽  
Theres Oakes ◽  
Bram Gerritsen ◽  
Mazlina Ismail ◽  
James M Heather ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Cell Repertoire ◽  
Clone Size ◽  
Receptor Repertoire ◽  
Tcr Repertoire ◽  
Naïve T Cell ◽  
Cell Receptor Repertoire ◽  
Naive T Cell

The clone size distribution of the human naive T-cell receptor (TCR) repertoire is an important determinant of adaptive immunity. We estimated the abundance of TCR sequences in samples of naive T cells from blood using an accurate quantitative sequencing protocol. We observe most TCR sequences only once, consistent with the enormous diversity of the repertoire. However, a substantial number of sequences were observed multiple times. We detect abundant TCR sequences even after exclusion of methodological confounders such as sort contamination, and multiple mRNA sampling from the same cell. By combining experimental data with predictions from models we describe two mechanisms contributing to TCR sequence abundance. TCRα abundant sequences can be primarily attributed to many identical recombination events in different cells, while abundant TCRβ sequences are primarily derived from large clones, which make up a small percentage of the naive repertoire, and could be established early in the development of the T-cell repertoire.

scholarly journals STIG: Generation and simulated sequencing of synthetic T cell receptor repertoires

2020 ◽  
Author(s):  
Mark G. Woodcock ◽  
Dante S. Bortone ◽  
Benjamin G. Vincent
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Relative Size ◽  
Cell Receptor ◽  
T Cell Repertoire ◽  
Sequencing Data ◽  
Cell Repertoire ◽  
Genetic Sequencing ◽  
Dna And Rna

AbstractT cell receptor repertoire inference from DNA and RNA sequencing experiments is frequently performed to characterize host immune responses to disease states. Existing tools for repertoire inference have been compared across publicly available biological datasets or unpublished simulated sequencing data. Evaluation and comparison of these tools is challenging without common data sets created from a known repertoire with well-defined biological and sequencing characteristics. Here we introduce STIG, a tool to create simulated T cell receptor sequencing data from a customizable virtual T cell repertoire, with clear attribution of individual reads back to locations within their respective T-cell receptor clonotypes. STIG allows for robust performance evaluation of T cell repertoire inference and downstream analysis methods. STIG is implemented in Python 3 and is freely available for download at https://github.com/Benjamin-Vincent-Lab/stigAuthor summaryAs part of the acquired immune system, T cells are integral in the host response to microbes, tumors and autoimmune disease. These cells each have a semi-unique T cell receptor that serves to bind a set of antigens that will in turn stimulate that cell to perform its particular pro- (or anti) inflammatory role. This receptor is the product of DNA rearrangement of germline gene segments, similar to B cell receptor loci rearrangement, which provides a wide variety of potential T cell receptors to respond to antigens. At the site of an immune reaction, T cells can increase their number through clonal expansion and methods have been developed to analyze bulk genetic sequencing data to infer the individual receptors and the relative size of their clonal subpopulations present within a sample. To date, these methods and tools have been tested and compared using either biological samples (where the true quantitiy and types of T cells is unknown) or unshared synthetic datasets. In this paper I describe a new tool to generate biologically-inspired T-cell repertoires in-silico and generate simulated sequencing data from them.

Improved T-cell Receptor Diversity Estimates Associate with Survival and Response to Anti–PD-1 Therapy

2020 ◽  
Vol 9 (1) ◽  
pp. 103-112
Author(s):  
Dante S. Bortone ◽  
Mark G. Woodcock ◽  
Joel S. Parker ◽  
Benjamin G. Vincent
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Cell Receptor ◽  
Diversity Estimates

scholarly journals Human T cell receptor occurrence patterns encode immune history, genetic background, and receptor specificity

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
William S DeWitt ◽  
Anajane Smith ◽  
Gary Schoch ◽  
John A Hansen ◽  
Frederick A Matsen ◽  
...  
Keyword(s):  
T Cell ◽  
T Cell Receptor ◽  
Genetic Background ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Human T Cell ◽  
Disease Associations ◽  
Repertoire Sequencing ◽  
Mhc Polymorphism ◽  
Occurrence Patterns

The T cell receptor (TCR) repertoire encodes immune exposure history through the dynamic formation of immunological memory. Statistical analysis of repertoire sequencing data has the potential to decode disease associations from large cohorts with measured phenotypes. However, the repertoire perturbation induced by a given immunological challenge is conditioned on genetic background via major histocompatibility complex (MHC) polymorphism. We explore associations between MHC alleles, immune exposures, and shared TCRs in a large human cohort. Using a previously published repertoire sequencing dataset augmented with high-resolution MHC genotyping, our analysis reveals rich structure: striking imprints of common pathogens, clusters of co-occurring TCRs that may represent markers of shared immune exposures, and substantial variations in TCR-MHC association strength across MHC loci. Guided by atomic contacts in solved TCR:peptide-MHC structures, we identify sequence covariation between TCR and MHC. These insights and our analysis framework lay the groundwork for further explorations into TCR diversity.

scholarly journals Characterization of Changes in the T-Cell Receptor Repertoire in Patients with Acute Myeloid Leukemia with Durable Remission Following Allogeneic Stem Cell Transplant

Blood ◽  
2019 ◽  
Vol 134 (Supplement_1) ◽  
pp. 5186-5186
Author(s):  
Ronald M. Paranal ◽  
Hagop M. Kantarjian ◽  
Alexandre Reuben ◽  
Celine Kerros ◽  
Priya Koppikar ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Myeloid Leukemia ◽  
Research Funding ◽  
Cell Receptor ◽  
T Cell Repertoire ◽  
Cell Repertoire ◽  
Donor T Cells ◽  
Durable Remission

Introduction: Allogeneic hematopoietic stem-cell transplantation (HSCT) is curative for many patients with advanced hematologic cancers, including adverse-risk acute myeloid leukemia (AML). This is principally through the induction of a graft-versus-leukemia (GVL) immune effect, mediated by donor T-cells. The incredible diversity and specificity of T-cells is due to rearrangement between V, D, and J regions and the random insertion/deletion of nucleotides, taking place in the hypervariable complementarity determining region 3 (CD3) of the T-cell receptor (TCR). Massively parallel sequencing of CDR3 allows for a detailed understanding of the T-cell repertoire, an area relatively unexplored in AML. Therefore, we sought out to characterize the T-cell repertoire in AML before and after HSCT, specifically for those with a durable remission. Methods: We identified 45 bone marrow biopsy samples, paired pre- and post-HSCT, from 14 patients with AML in remission for > 2 years as of last follow-up. We next performed immunosequencing of the TCRβ repertoire (Adaptive Biotechnologies). DNA was amplified in a bias-controlled multiplex PCR, resulting in amplification of rearranged VDJ segments, followed by high-throughput sequencing. Resultant sequences were collapsed and filtered in order to identify and quantitate the absolute abundance of each unique TCRβ CDR3 region. We next employed various metrics to characterize changes in the TCR repertoire: (1) clonality (range: 0-1; values closer to 1 indicate a more oligoclonal repertoire), it accounts for both the number of unique clonotypes and the extent to which a few clonotypes dominate the repertoire; (2) richness with a higher number indicating a more diverse repertoire with more unique rearrangements); (3) overlap (range: 0-1; with 1 being an identical T-cell repertoire). All calculations were done using the ImmunoSeq Analyzer software. Results: The median age of patients included in this cohort was 58 years (range: 31-69). Six patient (43%) had a matched related donor, and 8 (57%) had a matched unrelated donor. Baseline characteristics are summarized in Figure 1A. Six samples were excluded from further analysis due to quality. TCR richness did not differ comparing pre- and post-HSCT, with a median number pre-HSCT of 3566 unique sequences (range: 1282-22509) vs 3720 (range: 1540-12879) post-HSCT (P = 0.7). In order to assess whether there was expansion of certain T-cell clones following HSCT, we employed several metrics and all were indicative of an increase in clonality (Figure 2B). Productive clonality, a measure of reactivity, was significantly higher in post-transplant samples (0.09 vs 0.02, P = 0.003). This is a measure that would predict expansion of sequences likely to produce functional TCRs. The Maximum Productive Frequency Index was higher post-HSCT indicating that the increase in clonality was driven by the top clone (most prevalent per sample). Similarly for the Simpson's Dominance index, another marker of clonality which was higher post-HSCT (0.01 vs 0.0009, P = 0.04). In order to determine whether this clonal expansion was driven by TCR clones shared among patients, we compared the degree of overlap in unique sequences among pre and post-HSCT samples. We found there was very little overlap between samples in the pre and the post-transplant setting and no change in the Morisita and Jaccard Overlap Indices. Conclusions: In conclusion, we show in this analysis an increase in clonality of T-cells following HSCT in patients with AML. This is likely related to the GVL effect after recognition of leukemia antigens by donor T cells and subsequent expansion of these T-cells. These expanded T-cell clonotypes were unlikely to be shared by patients in this cohort, likely reflecting the variety of antigens leading to the GVL effect. This could have direct implications on TCR-mediated immune-therapies given the likely need for a personalized, patient-specific design for these therapies. Figure 1 Disclosures Kantarjian: BMS: Research Funding; Novartis: Research Funding; AbbVie: Honoraria, Research Funding; Jazz Pharma: Research Funding; Astex: Research Funding; Immunogen: Research Funding; Actinium: Honoraria, Membership on an entity's Board of Directors or advisory committees; Agios: Honoraria, Research Funding; Daiichi-Sankyo: Research Funding; Takeda: Honoraria; Amgen: Honoraria, Research Funding; Cyclacel: Research Funding; Ariad: Research Funding; Pfizer: Honoraria, Research Funding. Short:Takeda Oncology: Consultancy, Research Funding; AstraZeneca: Consultancy; Amgen: Honoraria. Cortes:Takeda: Consultancy, Research Funding; Bristol-Myers Squibb: Consultancy, Research Funding; Jazz Pharmaceuticals: Consultancy, Research Funding; Sun Pharma: Research Funding; BiolineRx: Consultancy; Novartis: Consultancy, Honoraria, Research Funding; Astellas Pharma: Consultancy, Honoraria, Research Funding; Merus: Consultancy, Honoraria, Research Funding; Immunogen: Consultancy, Honoraria, Research Funding; Biopath Holdings: Consultancy, Honoraria; Daiichi Sankyo: Consultancy, Honoraria, Research Funding; Pfizer: Consultancy, Honoraria, Research Funding; Forma Therapeutics: Consultancy, Honoraria, Research Funding. Jabbour:Cyclacel LTD: Research Funding; Pfizer: Consultancy, Research Funding; Amgen: Consultancy, Research Funding; AbbVie: Consultancy, Research Funding; Takeda: Consultancy, Research Funding; BMS: Consultancy, Research Funding; Adaptive: Consultancy, Research Funding. Molldrem:M. D. Anderson & Astellas Pharma: Other: Royalties.

Canonical T cell receptor docking on peptide–MHC is essential for T cell signaling

Science ◽  
2021 ◽  
Vol 372 (6546) ◽  
pp. eabe9124
Author(s):  
Pirooz Zareie ◽  
Christopher Szeto ◽  
Carine Farenc ◽  
Sachith D. Gunasinghe ◽  
Elizabeth M. Kolawole ◽  
...  
Keyword(s):  
T Cell ◽  
Cell Signaling ◽  
T Cell Receptor ◽  
T Cell Activation ◽  
Cell Activation ◽  
Direct Consequence ◽  
Cell Receptor ◽  
T Cell Signaling ◽  
Cell Repertoire

T cell receptor (TCR) recognition of peptide–major histocompatibility complexes (pMHCs) is characterized by a highly conserved docking polarity. Whether this polarity is driven by recognition or signaling constraints remains unclear. Using “reversed-docking” TCRβ-variable (TRBV) 17+ TCRs from the naïve mouse CD8+ T cell repertoire that recognizes the H-2Db–NP366 epitope, we demonstrate that their inability to support T cell activation and in vivo recruitment is a direct consequence of reversed docking polarity and not TCR–pMHCI binding or clustering characteristics. Canonical TCR–pMHCI docking optimally localizes CD8/Lck to the CD3 complex, which is prevented by reversed TCR–pMHCI polarity. The requirement for canonical docking was circumvented by dissociating Lck from CD8. Thus, the consensus TCR–pMHC docking topology is mandated by T cell signaling constraints.

Systematic comparative study of computational methods for T-cell receptor sequencing data analysis

2017 ◽  
Vol 20 (1) ◽  
pp. 222-234 ◽  
Author(s):  
Saira Afzal ◽  
Irene Gil-Farina ◽  
Richard Gabriel ◽  
Shahzad Ahmad ◽  
Christof von Kalle ◽  
...  
Keyword(s):  
Data Analysis ◽  
T Cell ◽  
Comparative Study ◽  
T Cell Receptor ◽  
Computational Methods ◽  
Cell Receptor ◽  
Sequencing Data ◽  
Sequencing Data Analysis

scholarly journals Diversification of the antigen-specific T cell receptor repertoire after varicella zoster vaccination

2016 ◽  
Vol 8 (332) ◽  
pp. 332ra46-332ra46 ◽  
Author(s):  
Qian Qi ◽  
Mary M. Cavanagh ◽  
Sabine Le Saux ◽  
Hong NamKoong ◽  
Chulwoo Kim ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
T Cell Receptor ◽  
Chronic Infection ◽  
Cell Receptor ◽  
Control Mechanisms ◽  
Cell Repertoire ◽  
T Cell Clones ◽  
Varicella Zoster ◽  
Cell Clones

Diversity and size of the antigen-specific T cell receptor (TCR) repertoire are two critical determinants for successful control of chronic infection. Varicella zoster virus (VZV) that establishes latency during childhood can escape control mechanisms, in particular with increasing age. We examined the TCR diversity of VZV-reactive CD4 T cells in individuals older than 50 years by studying three identical twin pairs and three unrelated individuals before and after vaccination with live attenuated VZV. Although all individuals had a small number of dominant T cell clones, the breadth of the VZV-specific repertoire differed markedly. A genetic influence was seen for the sharing of individual TCR sequences from antigen-reactive cells but not for repertoire richness or the selection of dominant clones. VZV vaccination favored the expansion of infrequent VZV antigen–reactive TCRs, including those from naïve T cells with lesser boosting of dominant T cell clones. Thus, vaccination does not reinforce the in vivo selection that occurred during chronic infection but leads to a diversification of the VZV-reactive T cell repertoire. However, a single-booster immunization seems insufficient to establish new clonal dominance. Our results suggest that repertoire analysis of antigen-specific TCRs can be an important readout to assess whether a vaccination was able to generate memory cells in clonal sizes that are necessary for immune protection.

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